The DOI matches its Crossref record; 99 of the 102 references it cites were found in a registry or an index.
Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?
Open accessAbstract
The amount of data collected and managed in (bio)medicine is ever-increasing. Thus, there is a need to rapidly and efficiently collect, analyze, and characterize all this information. Artificial intelligence (AI), with an emphasis on deep learning, holds great promise in this area and is already being successfully applied to basic research, diagnosis, drug discovery, and clinical trials. Rare diseases (RDs), which are severely underrepresented in basic and clinical research, can particularly benefit from AI technologies. Of the more than 7000 RDs described worldwide, only 5% have a treatment. The ability of AI technologies to integrate and analyze data from different sources (e.g., multi-omics, patient registries, and so on) can be used to overcome RDs’ challenges (e.g., low diagnostic rates, reduced number of patients, geographical dispersion, and so on). Ultimately, RDs’ AI-mediated knowledge could significantly boost therapy development. Presently, there are AI approaches being used in RDs and this review aims to collect and summarize these advances. A section dedicated to congenital disorders of glycosylation (CDG), a particular group of orphan RDs that can serve as a potential study model for other common diseases and RDs, has also been included.
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“Surveys show that patient satisfaction, provider training, research and quality improvement activities vary among medical centers (739,740);”
From Congenital Adrenal Hyperplasia—Current Insights in Pathophysiology, Diagnostics, and Management (2022) · cites this paper as [739]
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Cited by
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Congenital Adrenal Hyperplasia—Current Insights in Pathophysiology, Diagnostics, and Management
Claahsen - van der Grinten, H. L., Speiser, P. W., Ahmed, S. F., Arlt, W., Auchus, R. J., Falhammar, H., Flück, C. E., Guasti, L., Huebner, A., Kortmann, B. B. M., et al. (2022) · Endocrine Reviews · doi:10.1210/endrev/bnab016 · Cited by 556
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Artificial intelligence in rare disease diagnosis and treatment
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Metadata
- Authors
- Brasil, S., Pascoal, C., Francisco, R., dos Reis Ferreira, V., A. Videira, P., & Valadão, G.
- Affiliations
- Universidade Nova de Lisboa (PT)
- Portuguese Association for CDG, 2820-381 Lisboa, Portugal
- Universidade Politécnica de Lisboa (PT)
- Venue
- Genes
- Publisher
- MDPI AG
- Published
- 2019-11-27
- Volume
- 10
- Issue
- 12
- Pages
- 978
- ISSN
- 2073-4425 (ISSN-L)
- PMC
- PMC6947640
- OpenAlex
- W2989824398
- Cited by
- 160
- References
- 102
- Citations by year
- 2026: 17 · 2025: 41 · 2024: 28 · 2023: 22 · 2022: 21 · 2021: 20 · 2020: 11
- FWCI
- 0.64
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Genes (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, other-oa)
- Funders
- Fundação para a Ciência e a Tecnologia (SFRH/BD/138647/2018, SFRH/BD/124326/2016)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- artificial intelligence, rare diseases, deep learning, congenital disorders of glycosylation, multi-omics integration, patient registry, drug discovery, diagnosis, clinical trials, translational medicine
- MeSH terms
- Deep Learning, Clinical Trials as Topic, Humans, Rare Diseases, Drug Discovery, Precision Medicine
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